İnmeli Hastalarda Üst Ekstremite Hareket Başarımının Yapay Öğrenme Teknikleri ile Kestirimi

Öz

İnme benzeri nörolojik hastalıklardan sonra kişilerin rehabilite hizmetlerine yönlendirilmesinin ana nedeni, bireylerin günlük yaşamdaki yeteneklerini normal düzeye çıkartmaktır. Kişilerin günlük yaşamlarındaki faaliyetlerini ölçmek bu rehabilitasyon hizmetlerinin daha sağlıklı ilerlemesini sağlamaktadır. Çalışmamızda doktorlar tarafından rehabilitasyon sırasında inme hastalarının hareket işlevini değerlendirmek için yaygın olarak kullanılan Brunnstrom Evrelemesi incelenmiştir. Çalışma özgül olarak Brunnstrom Evrelemesi testinin üst ekstremite bölümü 4a hareketine uyarlanmıştır. Hastaların günlük hareketleri ivmeölçerler sensörleri ile değerlendirilmiştir. Bu çalışmada izlenilen metodoloji ile gönüllü 15 inme hastası ve 80 sağlıklı bireyden sensör datası toplanmıştır. Elde edilen sensör verisi medikal uzman tarafından yorumlanmıştır. Böylece sağlıklı ve hasta bireylerin hareket verileri arasındaki tutarlılıklar analiz edilmiştir. Analiz işlemi sonucunda elde edilen veriler yapay öğrenme yöntemleriyle incelenmiş ve sağlıklı/sağlıksız şeklinde sınıflandırılmıştır. Çalışmanın metodolojisi kişilerin günlük yaşamlarında üst/alt ekstremite performansını arttırmak için tasarlanmış araştırmalar için uygundur.

Anahtar Kelimeler

• Brunnstrom Evrelemesi
• İnme
• Makine Öğrenmesi
• İvmeölçer

Estimation of Upper Extremity Movement Performance in Stroke Patients with Artificial Learning Techniques

Öz

The main reason why people are directed to rehabilitation services after stroke-like neurological diseases are to bring individuals' daily abilities to a normal level. Measuring the activities of people in their daily lives ensures that these rehabilitation services progress more healthily. In our study, Brunnstrom Hemiplegia Recovery Staging, which is widely used by doctors to evaluate the movement function of stroke patients during rehabilitation, was examined. The study was specifically adapted to the upper extremity stage 4a movement of the Brunnstrom Staging. Daily movements of patients were evaluated with accelerometer sensors. With this methodology, sensor data was collected from 15 volunteer stroke patients and 80 healthy individuals. These sensor data were interpreted by the medical professional. Thus, consistency between movement data of healthy and sick individuals was analyzed. The data obtained as a result of the analysis process were examined with artificial learning methods and classified as healthy/unhealthy. The methodology of the study is suitable for research designed to increase upper / lower extremity performance in the daily life of individuals.

Anahtar Kelimeler

• Brunnstrom Staging
• Stroke
• Machine Learning
• Accelerometer

Kaynakça

1. [1] Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart Disease and Stroke Statistics - 2014 Update: A report from the American Heart Association. Circulation 2014. https://doi.org/10.1161/01.cir.0000441139.02102.80.
2. [2] Songül DEVRİM SOYDEMİR. Acilde serebral inme endikasyonu ile yoğun bakım yatış kararı verilen kronik hemodiyaliz hastalarının demografik özellikleri, Natıonal ınstıtutes of health stroke skalası (nıhss) ve charlson komorbidite skorlarının (ccs) diğer hastalar ile karşılaştırılma. 2015.
3. [3] Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, et al. Executive summary: Heart disease and stroke statistics-2010 update: A report from the american heart association. Circulation 2010. https://doi.org/10.1161/CIRCULATIONAHA.109.192667.
4. [4] Gustavsson A, Svensson M, Jacobi F, Allgulander C, Alonso J, Beghi E, et al. Corrigendum to “Cost of disorders of the brain in Europe 2010” [Eur. Neuropsychopharmacol. 21 (2011) 718-779]. Eur Neuropsychopharmacol 2012. https://doi.org/10.1016/j.euroneuro.2012.01.001.
5. [5] Hancock N, Kilbride C. National clinical guideline for stroke. R Coll Physicians, UK 2012.
6. [6] Lange B, Chang CY, Suma E, Newman B, Rizzo AS, Bolas M. Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 2011. https://doi.org/10.1109/IEMBS.2011.6090521.
7. [7] Rosenbaum P, Stewart D. The World Health Organization International Classification of Functioning, Disability, and Health: A Model to Guide Clinical Thinking, Practice and Research in the Field of Cerebral Palsy. Semin Pediatr Neurol 2004. https://doi.org/10.1016/j.spen.2004.01.002.
8. [8] Tripoliti EE, Zervakis M, Fotiadis DI. Computer-based assessment of alzheimer’s disease employing fMRI and/or EEG: A comprehensive review. Mod. Electroencephalogr. Assess. Tech. Theory Appl., 2014. https://doi.org/10.1007/7657_2014_70.

9. [9] Rigas G, Tzallas AT, Tsipouras MG, Bougia P, Tripoliti EE, Baga D, et al. Assessment of tremor activity in the parkinsons disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed 2012. https://doi.org/10.1109/TITB.2011.2182616.
10. [10] Mazilu S, Blanke U, Hardegger M, Troster G, Gazit E, Dorfman M, et al. GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson’s disease. 2014 IEEE Int. Conf. Pervasive Comput. Commun. Work. PERCOM Work. 2014, 2014. https://doi.org/10.1109/PerComW.2014.6815179.
11. [11] Ahlrichs C, Samà A, Lawo M, Cabestany J, Rodríguez-Martín D, Pérez-López C, et al. Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients. Med Biol Eng Comput 2016. https://doi.org/10.1007/s11517-015-1395-3.
12. [12] Harun GÜNEŞ. Eeg/Emg Türü Zaman Serileri Kullanılarak Hareket Sınıflandırma İçin Derin Öğrenme Kullanımı. Fırat Üniversitesi, 2019.
13. [13] Ossig C, Antonini A, Buhmann C, Classen J, Csoti I, Falkenburger B, et al. Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease. J Neural Transm 2016. https://doi.org/10.1007/s00702-015-1439-8.
14. [14] Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, et al. Monitoring motor fluctuations in patients with parkinsons disease using wearable sensors. IEEE Trans Inf Technol Biomed 2009. https://doi.org/10.1109/TITB.2009.2033471.
15. [15] Mamorita N, Iizuka T, Takeuchi A, Shirataka M, Ikeda N. Development of a system for measurement and analysis of tremor using a three-axis accelerometer. Methods Inf. Med., 2009. https://doi.org/10.3414/ME9243.
16. [16] Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 2014. https://doi.org/10.1016/j.gaitpost.2014.03.189.
17. [17] Rudzińska M, Izworski A, Banaszkiewicz K, Bukowczan S, Marona M, Szczudlik A. Quantitative tremor measurement with the computerized analysis of spiral drawing. Neurol Neurochir Pol 2007.
18. [18] O’Suilleabhain PE, Dewey RB. Validation for tremor quantification of an electromagnetic tracking device. Mov Disord 2001. https://doi.org/10.1002/mds.1064.
19. [19] Norman KE, Edwards R, Beuter A. The measurement of tremor using a velocity transducer: Comparison to simultaneous recordings using transducers of displacement, acceleration and muscle activity. J Neurosci Methods 1999. https://doi.org/10.1016/S0165-0270(99)00091-6.
20. [20] Carod-Artal FJ, Egido JA. Quality of life after stroke: The importance of a good recovery. Cerebrovasc. Dis., 2009. https://doi.org/10.1159/000200461.
21. [21] Hajibandeh N, Faghihi F, Ranjbar H, Kazari H. Classifications of disturbances using wavelet transform and support vector machine. Turkish J Electr Eng Comput Sci 2017. https://doi.org/10.3906/elk-1511-124.